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SDHAR-HOME: A Sensor Dataset for Human Activity Recognition at Home.

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  • 1CARTIF, Technological Center, 47151 Valladolid, Spain.

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Summary

This study developed a non-intrusive home monitoring system using sensors and activity wristbands to track elderly daily habits. Deep learning models achieved high accuracy in recognizing user activities, improving independent living safety.

Keywords:
activity wristbandsbeaconsdatasetdeep learningneural networksensorssmart home

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Area of Science:

  • Gerontology
  • Health Informatics
  • Artificial Intelligence

Background:

  • Improving the quality of life for the elderly, particularly those living alone, is a key health research objective.
  • Elderly individuals may face risks at home due to physical, sensory, or cognitive limitations.
  • Non-intrusive monitoring systems can help detect and mitigate potential dangers for seniors.

Purpose of the Study:

  • To develop a non-intrusive home-based database for monitoring elderly residents.
  • To combine sensor data, indoor positioning, and wearable activity trackers for comprehensive user monitoring.
  • To validate the system's effectiveness using real-time activity recognition with advanced machine learning techniques.

Main Methods:

  • Utilized a combination of non-intrusive sensors, triangulation-based indoor positioning (beacons), and activity wristbands.
  • Collected two months of continuous data on the daily habits of two elderly individuals.
  • Applied Deep Learning (DL) techniques, including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) networks for activity recognition.

Main Results:

  • Successfully labelled 18 distinct daily activities.
  • Developed personalized prediction models for each user, achieving high hit rates between 88.29% and 90.91%.
  • Implemented a data-sharing algorithm to enhance model generalizability and prevent neural network overtraining.

Conclusions:

  • The developed system effectively monitors elderly individuals' daily activities in their homes using non-intrusive technology.
  • Deep learning models demonstrate significant accuracy in recognizing activities, supporting independent living.
  • The data-sharing algorithm contributes to more robust and generalizable activity recognition models for elder care.